Home based augmented reality shopping

ABSTRACT

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and a method for performing operations comprising: receiving a video that includes a depiction of one or more objects in a room within a home; determining a room classification for the room by processing the one or more objects depicted in the video; selecting one or more augmented reality items available for purchase based on the room classification and the one or more objects depicted in the video; and generating, for display within the video, the one or more augmented reality items that have been selected at a display position within the video corresponding to the one or more objects depicted in the video.

TECHNICAL FIELD

The present disclosure relates generally to providing augmented realityexperiences using a messaging application.

BACKGROUND

Augmented-Reality (AR) is a modification of a virtual environment. Forexample, in Virtual Reality (VR), a user is completely immersed in avirtual world, whereas in AR, the user is immersed in a world wherevirtual objects are combined or superimposed on the real world. An ARsystem aims to generate and present virtual objects that interactrealistically with a real-world environment and with each other.Examples of AR applications can include single or multiple player videogames, instant messaging systems, and the like.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some nonlimiting examples areillustrated in the figures of the accompanying drawings in which:

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexamples.

FIG. 2 is a diagrammatic representation of a messaging clientapplication, in accordance with some examples.

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some examples.

FIG. 4 is a diagrammatic representation of a message, in accordance withsome examples.

FIG. 5 is a block diagram showing an example AR recommendation system,according to example examples.

FIGS. 6-9 are diagrammatic representations of outputs of the ARrecommendation system, in accordance with some examples.

FIG. 10 is a flowchart illustrating example operations of the messagingapplication server, according to examples.

FIG. 11 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples.

FIG. 12 is a block diagram showing a software architecture within whichexamples may be implemented.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative examples of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of various examples.It will be evident, however, to those skilled in the art, that examplesmay be practiced without these specific details. In general, well-knowninstruction instances, protocols, structures, and techniques are notnecessarily shown in detail.

Typically, virtual reality (VR) and augmented reality (AR) systems allowusers to add augmented reality elements to their environment (e.g.,captured image data corresponding to a user's surroundings). Suchsystems can recommend AR elements based on various external factors,such as a current geographical location of the user and various othercontextual clues. Some AR systems allow a user to capture a video of aroom and select from a list of available AR elements to add to a room tosee how the selected AR element looks in the room. These systems allow auser to preview how a physical item looks at a particular location in auser's environment, which simplifies the purchasing process. While thesesystems generally work well, they require a user to manually selectwhich AR elements to display within the captured video. Specifically,the user of these systems has to spend a great deal of effort searchingthrough and navigating multiple user interfaces and pages of informationto identify an item of interest. Then the user has to manually positionthe selected item within view. These tasks can be daunting and timeconsuming, which detracts from the overall interest of using thesesystems and results in wasted resources.

The disclosed techniques improve the efficiency of using an electronicdevice which implements or otherwise accesses an AR/VR system byintelligently automatically determining what room or environment iswithin view of a camera and automatically recommending AR elements todisplay within the camera view, such as for a user to purchasecorresponding physical or electronically consumable items (e.g., videoitems, music items, or video game items). Specifically, the disclosedtechniques receive a video that includes a depiction of one or moreobjects in a room within a home. The disclosed techniques determine aroom classification for the room by processing the one or more objectsdepicted in the video and select one or more augmented reality itemsavailable for purchase based on the room classification and the one ormore objects depicted in the video. The disclosed techniques generate,for display within the video, the one or more augmented reality itemsthat have been selected at a display position within the videocorresponding to the one or more objects depicted in the video.

In some cases, the disclosed techniques train a neural networkclassifier to determine the room classification. To train the neuralnetwork classifier, the disclosed techniques receive training datacomprising a plurality of training images and ground truth roomclassifications for each of the plurality of training images, each ofthe plurality of training monocular images depicting a different room ina home, and apply the neural network classifier to a first trainingimage of the plurality of training images to estimate a roomclassification of the room in the home depicted in the first trainingimage. The disclosed techniques compute a deviation between theestimated room classification and the ground truth room classificationassociated with the first training image and update parameters of theneural network classifier based on the computed deviation.

In this way, the disclosed techniques can select and automaticallydisplay one or more AR elements corresponding to items available forpurchase in the current image or video without further input from auser. This improves the overall experience of the user in using theelectronic device and reduces the overall amount of system resourcesneeded to accomplish a task.

Networked Computing Environment

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102, each of which hosts a number of applications, including a messagingclient 104 and other external applications 109 (e.g., third-partyapplications). Each messaging client 104 is communicatively coupled toother instances of the messaging client 104 (e.g., hosted on respectiveother client devices 102), a messaging server system 108 and externalapp(s) servers 110 via a network 112 (e.g., the Internet). A messagingclient 104 can also communicate with locally-hosted third-partyapplications (also referred to as “external applications” and “externalapps”) 109 using Applications Program Interfaces (APIs).

A messaging client 104 is able to communicate and exchange data withother messaging clients 104 and with the messaging server system 108 viathe network 112. The data exchanged between messaging clients 104, andbetween a messaging client 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 112 to a particular messaging client 104. While certainfunctions of the messaging system 100 are described herein as beingperformed by either a messaging client 104 or by the messaging serversystem 108, the location of certain functionality either within themessaging client 104 or the messaging server system 108 may be a designchoice. For example, it may be technically preferable to initiallydeploy certain technology and functionality within the messaging serversystem 108 but to later migrate this technology and functionality to themessaging client 104 where a client device 102 has sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client 104. Such operations includetransmitting data to, receiving data from, and processing data generatedby the messaging client 104. This data may include message content,client device information, geolocation information, media augmentationand overlays, message content persistence conditions, social networkinformation, and live event information, as examples. Data exchangeswithin the messaging system 100 are invoked and controlled throughfunctions available via user interfaces (UIs) of the messaging client104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 116 is coupled to, andprovides a programmatic interface to, application servers 114. Theapplication servers 114 are communicatively coupled to a database server120, which facilitates access to a database 126 that stores dataassociated with messages processed by the application servers 114.Similarly, a web server 128 is coupled to the application servers 114,and provides web-based interfaces to the application servers 114. Tothis end, the web server 128 processes incoming network requests overthe Hypertext Transfer Protocol (HTTP) and several other relatedprotocols.

The Application Program Interface (API) server 116 receives andtransmits message data (e.g., commands and message payloads) between theclient device 102 and the application servers 114. Specifically, theApplication Program Interface (API) server 116 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client 104 in order to invoke functionality of theapplication servers 114. The Application Program Interface (API) server116 exposes various functions supported by the application servers 114,including account registration, login functionality, the sending ofmessages, via the application servers 114, from a particular messagingclient 104 to another messaging client 104, the sending of media files(e.g., images or video) from a messaging client 104 to a messagingserver 118, and for possible access by another messaging client 104, thesettings of a collection of media data (e.g., story), the retrieval of alist of friends of a user of a client device 102, the retrieval of suchcollections, the retrieval of messages and content, the addition anddeletion of entities (e.g., friends) to an entity graph (e.g., a socialgraph), the location of friends within a social graph, and opening anapplication event (e.g., relating to the messaging client 104).

The application servers 114 host a number of server applications andsubsystems, including for example a messaging server 118, an imageprocessing server 122, and a social network server 124. The messagingserver 118 implements a number of message processing technologies andfunctions, particularly related to the aggregation and other processingof content (e.g., textual and multimedia content) included in messagesreceived from multiple instances of the messaging client 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available to themessaging client 104. Other processor- and memory-intensive processingof data may also be performed server-side by the messaging server 118,in view of the hardware requirements for such processing.

The application servers 114 also include an image processing server 122that is dedicated to performing various image processing operations,typically with respect to images or video within the payload of amessage sent from or received at the messaging server 118.

Image processing server 122 is used to implement scan functionality ofthe augmentation system 208 (shown in FIG. 2). Scan functionalityincludes activating and providing one or more augmented realityexperiences on a client device 102 when an image is captured by theclient device 102. Specifically, the messaging client 104 on the clientdevice 102 can be used to activate a camera. The camera displays one ormore real-time images or a video to a user along with one or more iconsor identifiers of one or more augmented reality experiences. The usercan select a given one of the identifiers to launch the correspondingaugmented reality experience or perform a desired image modification(e.g., launching a home-based AR shopping experience, as discussed inconnection with FIGS. 6-10 below).

The social network server 124 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server 118. To this end, the social network server 124maintains and accesses an entity graph 308 (as shown in FIG. 3) withinthe database 126. Examples of functions and services supported by thesocial network server 124 include the identification of other users ofthe messaging system 100 with which a particular user has relationshipsor is “following,” and also the identification of other entities andinterests of a particular user.

Returning to the messaging client 104, features and functions of anexternal resource (e.g., a third-party application 109 or applet) aremade available to a user via an interface of the messaging client 104.The messaging client 104 receives a user selection of an option tolaunch or access features of an external resource (e.g., a third-partyresource), such as external apps 109. The external resource may be athird-party application (external apps 109) installed on the clientdevice 102 (e.g., a “native app”), or a small-scale version of thethird-party application (e.g., an “applet”) that is hosted on the clientdevice 102 or remote of the client device 102 (e.g., on externalresource or app(s) servers 110). The small-scale version of thethird-party application includes a subset of features and functions ofthe third-party application (e.g., the full-scale, native version of thethird-party standalone application) and is implemented using amarkup-language document. In one example, the small-scale version of thethird-party application (e.g., an “applet”) is a web-based,markup-language version of the third-party application and is embeddedin the messaging client 104. In addition to using markup-languagedocuments (e.g., a .*ml file), an applet may incorporate a scriptinglanguage (e.g., a.*js file or a .json file) and a style sheet (e.g., a.*ss file).

In response to receiving a user selection of the option to launch oraccess features of the external resource (e.g., external app 109), themessaging client 104 determines whether the selected external resourceis a web-based external resource or a locally-installed externalapplication. In some cases, external applications 109 that are locallyinstalled on the client device 102 can be launched independently of andseparately from the messaging client 104, such as by selecting an icon,corresponding to the external application 109, on a home screen of theclient device 102. Small-scale versions of such external applicationscan be launched or accessed via the messaging client 104 and, in someexamples, no or limited portions of the small-scale external applicationcan be accessed outside of the messaging client 104. The small-scaleexternal application can be launched by the messaging client 104receiving, from an external app(s) server 110, a markup-languagedocument associated with the small-scale external application andprocessing such a document.

In response to determining that the external resource is alocally-installed external application 109, the messaging client 104instructs the client device 102 to launch the external application 109by executing locally-stored code corresponding to the externalapplication 109. In response to determining that the external resourceis a web-based resource, the messaging client 104 communicates with theexternal app(s) servers 110 to obtain a markup-language documentcorresponding to the selected resource. The messaging client 104 thenprocesses the obtained markup-language document to present the web-basedexternal resource within a user interface of the messaging client 104.

The messaging client 104 can notify a user of the client device 102, orother users related to such a user (e.g., “friends”), of activity takingplace in one or more external resources. For example, the messagingclient 104 can provide participants in a conversation (e.g., a chatsession) in the messaging client 104 with notifications relating to thecurrent or recent use of an external resource by one or more members ofa group of users. One or more users can be invited to join in an activeexternal resource or to launch a recently-used but currently inactive(in the group of friends) external resource. The external resource canprovide participants in a conversation, each using a respectivemessaging client messaging clients 104, with the ability to share anitem, status, state, or location in an external resource with one ormore members of a group of users into a chat session. The shared itemmay be an interactive chat card with which members of the chat caninteract, for example, to launch the corresponding external resource,view specific information within the external resource, or take themember of the chat to a specific location or state within the externalresource. Within a given external resource, response messages can besent to users on the messaging client 104. The external resource canselectively include different media items in the responses, based on acurrent context of the external resource.

The messaging client 104 can present a list of the available externalresources (e.g., third-party or external applications 109 or applets) toa user to launch or access a given external resource. This list can bepresented in a context-sensitive menu. For example, the iconsrepresenting different ones of the external applications 109 (orapplets) can vary based on how the menu is launched by the user (e.g.,from a conversation interface or from a non-conversation interface).

System Architecture

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to some examples. Specifically, themessaging system 100 is shown to comprise the messaging client 104 andthe application servers 114. The messaging system 100 embodies a numberof subsystems, which are supported on the client side by the messagingclient 104 and on the sever side by the application servers 114. Thesesubsystems include, for example, an ephemeral timer system 202, acollection management system 204, an augmentation system 208, a mapsystem 210, a game system 212, and an external resource system 220.

The ephemeral timer system 202 is responsible for enforcing thetemporary or time-limited access to content by the messaging client 104and the messaging server 118. The ephemeral timer system 202incorporates a number of timers that, based on duration and displayparameters associated with a message, or collection of messages (e.g., astory), selectively enable access (e.g., for presentation and display)to messages and associated content via the messaging client 104. Furtherdetails regarding the operation of the ephemeral timer system 202 areprovided below.

The collection management system 204 is responsible for managing sets orcollections of media (e.g., collections of text, image video, and audiodata). A collection of content (e.g., messages, including images, video,text, and audio) may be organized into an “event gallery” or an “eventstory.” Such a collection may be made available for a specified timeperiod, such as the duration of an event to which the content relates.For example, content relating to a music concert may be made availableas a “story” for the duration of that music concert. The collectionmanagement system 204 may also be responsible for publishing an iconthat provides notification of the existence of a particular collectionto the user interface of the messaging client 104.

The collection management system 204 furthermore includes a curationinterface 206 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface206 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certain examples,compensation may be paid to a user for the inclusion of user-generatedcontent into a collection. In such cases, the collection managementsystem 204 operates to automatically make payments to such users for theuse of their content.

The augmentation system 208 provides various functions that enable auser to augment (e.g., annotate or otherwise modify or edit) mediacontent associated with a message. For example, the augmentation system208 provides functions related to the generation and publishing of mediaoverlays for messages processed by the messaging system 100. Theaugmentation system 208 operatively supplies a media overlay oraugmentation (e.g., an image filter) to the messaging client 104 basedon a geolocation of the client device 102. In another example, theaugmentation system 208 operatively supplies a media overlay to themessaging client 104 based on other information, such as social networkinformation of the user of the client device 102. A media overlay mayinclude audio and visual content and visual effects. Examples of audioand visual content include pictures, texts, logos, animations, and soundeffects. An example of a visual effect includes color overlaying. Theaudio and visual content or the visual effects can be applied to a mediacontent item (e.g., a photo) at the client device 102. For example, themedia overlay may include text, a graphical element, or image that canbe overlaid on top of a photograph taken by the client device 102. Inanother example, the media overlay includes an identification of alocation overlay (e.g., Venice beach), a name of a live event, or a nameof a merchant overlay (e.g., Beach Coffee House). In another example,the augmentation system 208 uses the geolocation of the client device102 to identify a media overlay that includes the name of a merchant atthe geolocation of the client device 102. The media overlay may includeother indicia associated with the merchant. The media overlays may bestored in the database 126 and accessed through the database server 120.

In some examples, the augmentation system 208 provides a user-basedpublication platform that enables users to select a geolocation on a mapand upload content associated with the selected geolocation. The usermay also specify circumstances under which a particular media overlayshould be offered to other users. The augmentation system 208 generatesa media overlay that includes the uploaded content and associates theuploaded content with the selected geolocation.

In other examples, the augmentation system 208 provides a merchant-basedpublication platform that enables merchants to select a particular mediaoverlay associated with a geolocation via a bidding process. Forexample, the augmentation system 208 associates the media overlay of thehighest bidding merchant with a corresponding geolocation for apredefined amount of time. The augmentation system 208 communicates withthe image processing server 122 to obtain augmented reality experiencesand presents identifiers of such experiences in one or more userinterfaces (e.g., as icons over a real-time image or video or asthumbnails or icons in interfaces dedicated for presented identifiers ofaugmented reality experiences). Once an augmented reality experience isselected, one or more images, videos, or augmented reality graphicalelements are retrieved and presented as an overlay on top of the imagesor video captured by the client device 102. In some cases, the camera isswitched to a front-facing view (e.g., the front-facing camera of theclient device 102 is activated in response to activation of a particularaugmented reality experience) and the images from the front-facingcamera of the client device 102 start being displayed on the clientdevice 102 instead of the rear-facing camera of the client device 102.The one or more images, videos, or augmented reality graphical elementsare retrieved and presented as an overlay on top of the images that arecaptured and displayed by the front-facing camera of the client device102.

In other examples, the augmentation system 208 is able to communicateand exchange data with another augmentation system 208 on another clientdevice 102 and with the server via the network 112. The data exchangedcan include a session identifier that identifies the shared AR session,a transformation between a first client device 102 and a second clientdevice 102 (e.g., a plurality of client devices 102 include the firstand second devices) that is used to align the shared AR session to acommon point of origin, a common coordinate frame, functions (e.g.,commands to invoke functions) as well as other payload data (e.g., text,audio, video or other multimedia data).

The augmentation system 208 sends the transformation to the secondclient device 102 so that the second client device 102 can adjust the ARcoordinate system based on the transformation. In this way, the firstand second client devices 102 synch up their coordinate systems andframes for displaying content in the AR session. Specifically, theaugmentation system 208 computes the point of origin of the secondclient device 102 in the coordinate system of the first client device102. The augmentation system 208 can then determine an offset in thecoordinate system of the second client device 102 based on the positionof the point of origin from the perspective of the second client device102 in the coordinate system of the second client device 102. Thisoffset is used to generate the transformation so that the second clientdevice 102 generates AR content according to a common coordinate systemor frame as the first client device 102.

The augmentation system 208 can communicate with the client device 102to establish individual or shared AR sessions. The augmentation system208 can also be coupled to the messaging server 118 to establish anelectronic group communication session (e.g., group chat, instantmessaging) for the client devices 102 in a shared AR session. Theelectronic group communication session can be associated with a sessionidentifier provided by the client devices 102 to gain access to theelectronic group communication session and to the shared AR session. Inone example, the client devices 102 first gain access to the electronicgroup communication session and then obtain the session identifier inthe electronic group communication session that allows the clientdevices 102 to access to the shared AR session. In some examples, theclient devices 102 are able to access the shared AR session without aidor communication with the augmentation system 208 in the applicationservers 114.

The map system 210 provides various geographic location functions, andsupports the presentation of map-based media content and messages by themessaging client 104. For example, the map system 210 enables thedisplay of user icons or avatars (e.g., stored in profile data 316) on amap to indicate a current or past location of “friends” of a user, aswell as media content (e.g., collections of messages includingphotographs and videos) generated by such friends, within the context ofa map. For example, a message posted by a user to the messaging system100 from a specific geographic location may be displayed within thecontext of a map at that particular location to “friends” of a specificuser on a map interface of the messaging client 104. A user canfurthermore share his or her location and status information (e.g.,using an appropriate status avatar) with other users of the messagingsystem 100 via the messaging client 104, with this location and statusinformation being similarly displayed within the context of a mapinterface of the messaging client 104 to selected users.

The game system 212 provides various gaming functions within the contextof the messaging client 104. The messaging client 104 provides a gameinterface providing a list of available games (e.g., web-based games orweb-based applications) that can be launched by a user within thecontext of the messaging client 104, and played with other users of themessaging system 100. The messaging system 100 further enables aparticular user to invite other users to participate in the play of aspecific game, by issuing invitations to such other users from themessaging client 104. The messaging client 104 also supports both voiceand text messaging (e.g., chats) within the context of gameplay,provides a leaderboard for the games, and also supports the provision ofin-game rewards (e.g., coins and items).

The external resource system 220 provides an interface for the messagingclient 104 to communicate with external app(s) servers 110 to launch oraccess external resources. Each external resource (apps) server 110hosts, for example, a markup language (e.g., HTML5) based application orsmall-scale version of an external application (e.g., game, utility,payment, or ride-sharing application that is external to the messagingclient 104). The messaging client 104 may launch a web-based resource(e.g., application) by accessing the HTML5 file from the externalresource (apps) servers 110 associated with the web-based resource. Incertain examples, applications hosted by external resource servers 110are programmed in JavaScript leveraging a Software Development Kit (SDK)provided by the messaging server 118. The SDK includes ApplicationProgramming Interfaces (APIs) with functions that can be called orinvoked by the web-based application. In certain examples, the messagingserver 118 includes a JavaScript library that provides a giventhird-party resource access to certain user data of the messaging client104. HTML5 is used as an example technology for programming games, butapplications and resources programmed based on other technologies can beused.

In order to integrate the functions of the SDK into the web-basedresource, the SDK is downloaded by an external resource (apps) server110 from the messaging server 118 or is otherwise received by theexternal resource (apps) server 110. Once downloaded or received, theSDK is included as part of the application code of a web-based externalresource. The code of the web-based resource can then call or invokecertain functions of the SDK to integrate features of the messagingclient 104 into the web-based resource.

The SDK stored on the messaging server 118 effectively provides thebridge between an external resource (e.g., third-party or externalapplications 109 or applets and the messaging client 104). This providesthe user with a seamless experience of communicating with other users onthe messaging client 104, while also preserving the look and feel of themessaging client 104. To bridge communications between an externalresource and a messaging client 104, in certain examples, the SDKfacilitates communication between external resource servers 110 and themessaging client 104. In certain examples, a WebViewJavaScriptBridgerunning on a client device 102 establishes two one-way communicationchannels between an external resource and the messaging client 104.Messages are sent between the external resource and the messaging client104 via these communication channels asynchronously. Each SDK functioninvocation is sent as a message and callback. Each SDK function isimplemented by constructing a unique callback identifier and sending amessage with that callback identifier.

By using the SDK, not all information from the messaging client 104 isshared with external resource servers 110. The SDK limits whichinformation is shared based on the needs of the external resource. Incertain examples, each external resource server 110 provides an HTML5file corresponding to the web-based external resource to the messagingserver 118. The messaging server 118 can add a visual representation(such as a box art or other graphic) of the web-based external resourcein the messaging client 104. Once the user selects the visualrepresentation or instructs the messaging client 104 through a GUI ofthe messaging client 104 to access features of the web-based externalresource, the messaging client 104 obtains the HTML5 file andinstantiates the resources necessary to access the features of theweb-based external resource.

The messaging client 104 presents a graphical user interface (e.g., alanding page or title screen) for an external resource. During, before,or after presenting the landing page or title screen, the messagingclient 104 determines whether the launched external resource has beenpreviously authorized to access user data of the messaging client 104.In response to determining that the launched external resource has beenpreviously authorized to access user data of the messaging client 104,the messaging client 104 presents another graphical user interface ofthe external resource that includes functions and features of theexternal resource. In response to determining that the launched externalresource has not been previously authorized to access user data of themessaging client 104, after a threshold period of time (e.g., 3 seconds)of displaying the landing page or title screen of the external resource,the messaging client 104 slides up (e.g., animates a menu as surfacingfrom a bottom of the screen to a middle of or other portion of thescreen) a menu for authorizing the external resource to access the userdata. The menu identifies the type of user data that the externalresource will be authorized to use. In response to receiving a userselection of an accept option, the messaging client 104 adds theexternal resource to a list of authorized external resources and allowsthe external resource to access user data from the messaging client 104.In some examples, the external resource is authorized by the messagingclient 104 to access the user data in accordance with an OAuth 2framework.

The messaging client 104 controls the type of user data that is sharedwith external resources based on the type of external resource beingauthorized. For example, external resources that include full-scaleexternal applications (e.g., a third-party or external application 109)are provided with access to a first type of user data (e.g., onlytwo-dimensional avatars of users with or without different avatarcharacteristics). As another example, external resources that includesmall-scale versions of external applications (e.g., web-based versionsof third-party applications) are provided with access to a second typeof user data (e.g., payment information, two-dimensional avatars ofusers, three-dimensional avatars of users, and avatars with variousavatar characteristics). Avatar characteristics include different waysto customize a look and feel of an avatar, such as different poses,facial features, clothing, and so forth.

The AR recommendation system 224 receives an image or video from aclient device 102 that depicts a room in a home. The AR recommendationsystem 224 detects one or more real-world objects depicted in the imageor video and uses the detected one or more real-world objects (orfeatures of the room) to compute a classification for the room. Forexample, the AR recommendation system 224 can classify the room as akitchen, a bedroom, a nursery, a toddler room, a teenager room, anoffice, a living room, a den, a formal living room, a patio, a deck, abalcony, a bathroom, or any other suitable home-based roomclassification. Once classified, the AR recommendation system 224identifies one or more items (such as physical products orelectronically consumable content items) related to the roomclassification. The identified one or more items can be items that areavailable for purchase. The AR recommendation system 224 retrieves ARrepresentations of the identified items and incorporates (displays atspecified positions) the AR representations within the image or video.The AR representations can be interactive, such that upon receiving auser selection or input that selects the particular AR representation,an electronic commerce (e-commerce) purchase transaction is performed toobtain access to or receive the corresponding item. An illustrativeimplementation of the AR recommendation system 224 is shown anddescribed in connection with FIG. 5 below.

The AR recommendation system 224 is a component that can be accessed byan AR/VR application implemented on the client device 102. The AR/VRapplication uses an RGB camera to capture an image of a room in a home.The AR/VR application applies various trained machine learningtechniques on the captured image of the room to classify the room. TheAR/VR application includes a depth sensor to generate a virtual mesh ofthe room that is captured in order to incorporate or place into theimage or video the AR representations. For example, the AR/VRapplication can add an AR piece of furniture, such as an AR chair orsofa, to the image or video that is captured by the client device 102.In some implementations, the AR/VR application continuously capturesimages of the house or home in real time or periodically to continuouslyor periodically update the AR representations of items available forpurchase. This allows the user to move around in the real world and seeupdated AR representations of items available for purchase correspondingto a current room depicted in an image or video in real time.

Data Architecture

FIG. 3 is a schematic diagram illustrating data structures 300, whichmay be stored in the database 126 of the messaging server system 108,according to certain examples. While the content of the database 126 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 126 includes message data stored within a message table302. This message data includes, for any particular one message, atleast message sender data, message recipient (or receiver) data, and apayload. Further details regarding information that may be included in amessage, and included within the message data stored in the messagetable 302, are described below with reference to FIG. 4.

An entity table 306 stores entity data, and is linked (e.g.,referentially) to an entity graph 308 and profile data 316. Entities forwhich records are maintained within the entity table 306 may includeindividuals, corporate entities, organizations, objects, places, events,and so forth. Regardless of entity type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 308 stores information regarding relationships andassociations between entities. Such relationships may be social,professional (e.g., work at a common corporation or organization)interested-based or activity-based, merely for example.

The profile data 316 stores multiple types of profile data about aparticular entity. The profile data 316 may be selectively used andpresented to other users of the messaging system 100, based on privacysettings specified by a particular entity. Where the entity is anindividual, the profile data 316 includes, for example, a user name,telephone number, address, settings (e.g., notification and privacysettings), as well as a user-selected avatar representation (orcollection of such avatar representations). A particular user may thenselectively include one or more of these avatar representations withinthe content of messages communicated via the messaging system 100, andon map interfaces displayed by messaging clients 104 to other users. Thecollection of avatar representations may include “status avatars,” whichpresent a graphical representation of a status or activity that the usermay select to communicate at a particular time.

Where the entity is a group, the profile data 316 for the group maysimilarly include one or more avatar representations associated with thegroup, in addition to the group name, members, and various settings(e.g., notifications) for the relevant group.

The database 126 also stores augmentation data, such as overlays orfilters, in an augmentation table 310. The augmentation data isassociated with and applied to videos (for which data is stored in avideo table 304) and images (for which data is stored in an image table312).

The database 126 can also store data pertaining to individual and sharedAR sessions. This data can include data communicated between an ARsession client controller of a first client device 102 and another ARsession client controller of a second client device 102, and datacommunicated between the AR session client controller and theaugmentation system 208. Data can include data used to establish thecommon coordinate frame of the shared AR scene, the transformationbetween the devices, the session identifier, images depicting a body,skeletal joint positions, wrist joint positions, feet, and so forth.

Filters, in one example, are overlays that are displayed as overlaid onan image or video during presentation to a recipient user. Filters maybe of various types, including user-selected filters from a set offilters presented to a sending user by the messaging client 104 when thesending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client 104, based ongeolocation information determined by a Global Positioning System (GPS)unit of the client device 102.

Another type of filter is a data filter, which may be selectivelypresented to a sending user by the messaging client 104, based on otherinputs or information gathered by the client device 102 during themessage creation process. Examples of data filters include currenttemperature at a specific location, a current speed at which a sendinguser is traveling, battery life for a client device 102, or the currenttime.

Other augmentation data that may be stored within the image table 312includes augmented reality content items (e.g., corresponding toapplying augmented reality experiences). An augmented reality contentitem or augmented reality item may be a real-time special effect andsound that may be added to an image or a video.

As described above, augmentation data includes augmented reality contentitems, overlays, image transformations, AR images, and similar termsthat refer to modifications that may be applied to image data (e.g.,videos or images). This includes real-time modifications, which modifyan image as it is captured using device sensors (e.g., one or multiplecameras) of a client device 102 and then displayed on a screen of theclient device 102 with the modifications. This also includesmodifications to stored content, such as video clips in a gallery thatmay be modified. For example, in a client device 102 with access tomultiple augmented reality content items, a user can use a single videoclip with multiple augmented reality content items to see how thedifferent augmented reality content items will modify the stored clip.For example, multiple augmented reality content items that applydifferent pseudorandom movement models can be applied to the samecontent by selecting different augmented reality content items for thecontent. Similarly, real-time video capture may be used with anillustrated modification to show how video images currently beingcaptured by sensors of a client device 102 would modify the captureddata. Such data may simply be displayed on the screen and not stored inmemory, or the content captured by the device sensors may be recordedand stored in memory with or without the modifications (or both). Insome systems, a preview feature can show how different augmented realitycontent items will look within different windows in a display at thesame time. This can, for example, enable multiple windows with differentpseudorandom animations to be viewed on a display at the same time.

Data and various systems using augmented reality content items or othersuch transform systems to modify content using this data can thusinvolve detection of objects (e.g., faces, hands, bodies, cats, dogs,surfaces, objects, etc.), tracking of such objects as they leave, enter,and move around the field of view in video frames, and the modificationor transformation of such objects as they are tracked. In variousexamples, different methods for achieving such transformations may beused. Some examples may involve generating a three-dimensional meshmodel of the object or objects, and using transformations and animatedtextures of the model within the video to achieve the transformation. Inother examples, tracking of points on an object may be used to place animage or texture (which may be two dimensional or three dimensional) atthe tracked position. In still further examples, neural network analysisof video frames may be used to place images, models, or textures incontent (e.g., images or frames of video). Augmented reality contentitems thus refer both to the images, models, and textures used to createtransformations in content, as well as to additional modeling andanalysis information needed to achieve such transformations with objectdetection, tracking, and placement.

Real-time video processing can be performed with any kind of video data(e.g., video streams, video files, etc.) saved in a memory of acomputerized system of any kind. For example, a user can load videofiles and save them in a memory of a device, or can generate a videostream using sensors of the device. Additionally, any objects can beprocessed using a computer animation model, such as a human's face andparts of a human body, animals, or non-living things such as chairs,cars, or other objects.

In some examples, when a particular modification is selected along withcontent to be transformed, elements to be transformed are identified bythe computing device, and then detected and tracked if they are presentin the frames of the video. The elements of the object are modifiedaccording to the request for modification, thus transforming the framesof the video stream. Transformation of frames of a video stream can beperformed by different methods for different kinds of transformation.For example, for transformations of frames mostly referring to changingforms of an object's elements, characteristic points for each element ofan object are calculated (e.g., using an Active Shape Model (ASM) orother known methods). Then, a mesh based on the characteristic points isgenerated for each of the at least one element of the object. This meshis used in the following stage of tracking the elements of the object inthe video stream. In the process of tracking, the mentioned mesh foreach element is aligned with a position of each element. Then,additional points are generated on the mesh. A set of first points isgenerated for each element based on a request for modification, and aset of second points is generated for each element based on the set offirst points and the request for modification. Then, the frames of thevideo stream can be transformed by modifying the elements of the objecton the basis of the sets of first and second points and the mesh. Insuch a method, a background of the modified object can be changed ordistorted as well by tracking and modifying the background.

In some examples, transformations changing some areas of an object usingits elements can be performed by calculating characteristic points foreach element of an object and generating a mesh based on the calculatedcharacteristic points. Points are generated on the mesh, and thenvarious areas based on the points are generated. The elements of theobject are then tracked by aligning the area for each element with aposition for each of the at least one element, and properties of theareas can be modified based on the request for modification, thustransforming the frames of the video stream. Depending on the specificrequest for modification, properties of the mentioned areas can betransformed in different ways. Such modifications may involve changingcolor of areas; removing at least some part of areas from the frames ofthe video stream; including one or more new objects into areas which arebased on a request for modification; and modifying or distorting theelements of an area or object. In various examples, any combination ofsuch modifications or other similar modifications may be used. Forcertain models to be animated, some characteristic points can beselected as control points to be used in determining the entirestate-space of options for the model animation.

In some examples of a computer animation model to transform image datausing face detection, the face is detected on an image with use of aspecific face detection algorithm (e.g., Viola-Jones). Then, an ActiveShape Model (ASM) algorithm is applied to the face region of an image todetect facial feature reference points.

Other methods and algorithms suitable for face detection can be used.For example, in some examples, features are located using a landmark,which represents a distinguishable point present in most of the imagesunder consideration. For facial landmarks, for example, the location ofthe left eye pupil may be used. If an initial landmark is notidentifiable (e.g., if a person has an eyepatch), secondary landmarksmay be used. Such landmark identification procedures may be used for anysuch objects. In some examples, a set of landmarks forms a shape. Shapescan be represented as vectors using the coordinates of the points in theshape. One shape is aligned to another with a similarity transform(allowing translation, scaling, and rotation) that minimizes the averageEuclidean distance between shape points. The mean shape is the mean ofthe aligned training shapes.

In some examples, a search for landmarks from the mean shape aligned tothe position and size of the face determined by a global face detectoris started. Such a search then repeats the steps of suggesting atentative shape by adjusting the locations of shape points by templatematching of the image texture around each point and then conforming thetentative shape to a global shape model until convergence occurs. Insome systems, individual template matches are unreliable, and the shapemodel pools the results of the weak template matches to form a strongeroverall classifier. The entire search is repeated at each level in animage pyramid, from coarse to fine resolution.

A transformation system can capture an image or video stream on a clientdevice (e.g., the client device 102) and perform complex imagemanipulations locally on the client device 102 while maintaining asuitable user experience, computation time, and power consumption. Thecomplex image manipulations may include size and shape changes, emotiontransfers (e.g., changing a face from a frown to a smile), statetransfers (e.g., aging a subject, reducing apparent age, changinggender), style transfers, graphical element application, and any othersuitable image or video manipulation implemented by a convolutionalneural network that has been configured to execute efficiently on theclient device 102.

In some examples, a computer animation model to transform image data canbe used by a system where a user may capture an image or video stream ofthe user (e.g., a selfie) using a client device 102 having a neuralnetwork operating as part of a messaging client 104 operating on theclient device 102. The transformation system operating within themessaging client 104 determines the presence of a face within the imageor video stream and provides modification icons associated with acomputer animation model to transform image data, or the computeranimation model can be present as associated with an interface describedherein. The modification icons include changes that may be the basis formodifying the user's face within the image or video stream as part ofthe modification operation. Once a modification icon is selected, thetransformation system initiates a process to convert the image of theuser to reflect the selected modification icon (e.g., generate a smilingface on the user). A modified image or video stream may be presented ina graphical user interface displayed on the client device 102 as soon asthe image or video stream is captured, and a specified modification isselected. The transformation system may implement a complexconvolutional neural network on a portion of the image or video streamto generate and apply the selected modification. That is, the user maycapture the image or video stream and be presented with a modifiedresult in real-time or near real-time once a modification icon has beenselected. Further, the modification may be persistent while the videostream is being captured, and the selected modification icon remainstoggled. Machine-taught neural networks may be used to enable suchmodifications.

The graphical user interface, presenting the modification performed bythe transformation system, may supply the user with additionalinteraction options. Such options may be based on the interface used toinitiate the content capture and selection of a particular computeranimation model (e.g., initiation from a content creator userinterface). In various examples, a modification may be persistent afteran initial selection of a modification icon. The user may toggle themodification on or off by tapping or otherwise selecting the face beingmodified by the transformation system and store it for later viewing orbrowse to other areas of the imaging application. Where multiple facesare modified by the transformation system, the user may toggle themodification on or off globally by tapping or selecting a single facemodified and displayed within a graphical user interface. In someexamples, individual faces, among a group of multiple faces, may beindividually modified, or such modifications may be individually toggledby tapping or selecting the individual face or a series of individualfaces displayed within the graphical user interface.

A story table 314 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 306). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client 104 may include an icon that is user-selectableto enable a sending user to add specific content to his or her personalstory.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client 104, to contribute content to aparticular live story. The live story may be identified to the user bythe messaging client 104, based on his or her location. The end resultis a “live story” told from a community perspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some examples, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

As mentioned above, the video table 304 stores video data that, in oneexample, is associated with messages for which records are maintainedwithin the message table 302. Similarly, the image table 312 storesimage data associated with messages for which message data is stored inthe entity table 306. The entity table 306 may associate variousaugmentations from the augmentation table 310 with various images andvideos stored in the image table 312 and the video table 304.

The data structures 300 can also store training data for training one ormore machine learning techniques (models) to classify a room in a homeor household. The training data can include a plurality of images andvideos and their corresponding ground-truth room classifications. Theimages and videos can include a mix of all sorts of real-world objectsthat can appear in different rooms in a home or household. The one ormore machine learning techniques can be trained to extract features of areceived input image or video and establish a relationship between theextracted features and a room classification. Once trained, the machinelearning technique can receive a new image or video and can compute aroom classification for the newly received image or video.

The data structures 300 can also store a list or plurality of differentexpected objects for different room classifications. For example, thedata structures 300 can store a first list of expected objects for afirst room classification. Namely, a room classified as a kitchen can beassociated with a list of expected objects, such as appliances and/orfurniture items including: tea maker, toaster, kettle, mixer,refrigerator, blender, cabinet, cupboard, cooker hood, range hood,microwave, dish soap, kitchen counter, dinner table, kitchen scale,pedal bin, grill, and drawer. As another example, a room classified as aliving room can be associated with a list of expected objects including:wing chair, TV stand, sofa, cushion, telephone, television, speaker, endtable, tea set, fireplace, remote, fan, floor lamp, carpet, table,blinds, curtains, picture, vase, and grandfather clock.

As another example, a room classified as a bedroom can be associatedwith a list of expected objects, such as furniture items including:headboard, footboard and mattress frame, mattress and box springs,mattress pad, sheets and pillowcases, blankets, quilts, comforter,bedspread, duvet, bedskirt, sleeping pillows, specialty pillows,decorative pillows, pillow covers and shams, throws (blankets),draperies, rods, brackets, valances, window shades, blinds, shutters,nightstands, occasional tables; lamps; floor, table, hanging; wallsconces, alarm clock, radio, plants and plant containers, vases,flowers, candles, candleholders, artwork, posters, prints, photos,frames, photo albums, decorative objects and knick-knacks, dressers andclothing, armoire, closet, TV cabinet, chairs, loveseat, chaise lounge,ottoman, bookshelves, decorative ledges, books, magazines, bookends,trunk, bench, writing desk, vanity table, mirrors, rugs, jewelry boxesand jewelry, storage boxes, baskets, trays, telephone; television, cablebox, satellite box, DVD player and videos, tablets, nightlight.

Data Communications Architecture

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some examples, generated by a messaging client 104 forcommunication to a further messaging client 104 or the messaging server118. The content of a particular message 400 is used to populate themessage table 302 stored within the database 126, accessible by themessaging server 118. Similarly, the content of a message 400 is storedin memory as “in-transit” or “in-flight” data of the client device 102or the application servers 114. A message 400 is shown to include thefollowing example components:

-   -   message identifier 402: a unique identifier that identifies the        message 400.    -   message text payload 404: text, to be generated by a user via a        user interface of the client device 102, and that is included in        the message 400.    -   message image payload 406: image data, captured by a camera        component of a client device 102 or retrieved from a memory        component of a client device 102, and that is included in the        message 400. Image data for a sent or received message 400 may        be stored in the image table 312.    -   message video payload 408: video data, captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400. Video data        for a sent or received message 400 may be stored in the video        table 304.    -   message audio payload 410: audio data, captured by a microphone        or retrieved from a memory component of the client device 102,        and that is included in the message 400.    -   message augmentation data 412: augmentation data (e.g., filters,        stickers, or other annotations or enhancements) that represents        augmentations to be applied to message image payload 406,        message video payload 408, or message audio payload 410 of the        message 400. Augmentation data 412 for a sent or received        message 400 may be stored in the augmentation table 310.    -   message duration parameter 414: parameter value indicating, in        seconds, the amount of time for which content of the message        (e.g., the message image payload 406, message video payload 408,        message audio payload 410) is to be presented or made accessible        to a user via the messaging client 104.    -   message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message. Multiple message geolocation        parameter 416 values may be included in the payload, each of        these parameter values being associated with respect to content        items included in the content (e.g., a specific image within the        message image payload 406, or a specific video in the message        video payload 408).    -   message story identifier 418: identifier values identifying one        or more content collections (e.g., “stories” identified in the        story table 314) with which a particular content item in the        message image payload 406 of the message 400 is associated. For        example, multiple images within the message image payload 406        may each be associated with multiple content collections using        identifier values.    -   message tag 420: each message 400 may be tagged with multiple        tags, each of which is indicative of the subject matter of        content included in the message payload. For example, where a        particular image included in the message image payload 406        depicts an animal (e.g., a lion), a tag value may be included        within the message tag 420 that is indicative of the relevant        animal. Tag values may be generated manually, based on user        input, or may be automatically generated using, for example,        image recognition.    -   message sender identifier 422: an identifier (e.g., a messaging        system identifier, email address, or device identifier)        indicative of a user of the client device 102 on which the        message 400 was generated and from which the message 400 was        sent.    -   message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 312.Similarly, values within the message video payload 408 may point to datastored within a video table 304, values stored within the messageaugmentation data 412 may point to data stored in an augmentation table310, values stored within the message story identifier 418 may point todata stored in a story table 314, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within an entity table 306.

AR Recommendation System

FIG. 5 is a block diagram showing an example AR recommendation system224, according to example examples. The AR recommendation system 224includes a set of components 510 that operate on a set of input data(e.g., a monocular image (or video) depicting a room in a home 501 anddepth map data 502. The AR recommendation system 224 includes an objectdetection module 512, a room classification module 514, a depthreconstruction module 517, an expected object module 516, an imagemodification module 518, an AR item selection module 519, and an imagedisplay module 520. All or some of the components of the ARrecommendation system 224 can be implemented by a server, in which case,the monocular image depicting a room in a home 501 and the depth mapdata 502 are provided to the server by the client device 102. In somecases, some or all of the components of the AR recommendation system 224can be implemented by the client device 102.

The object detection module 512 receives a monocular image (or video)depicting a room in a home 501. This image can be received as part of areal-time video stream, a previously captured video stream or a newimage captured by a camera of the client device 102. The objectdetection module 512 applies one or more machine learning techniques toidentify real-world physical objects that appear in the image depictinga room in a home 501. For example, the object detection module 512 cansegment out individual objects in the image and assign a label or nameto the individual objects. Specifically, the object detection module 512can recognize a sofa as an individual object, a television as anotherindividual object, a light fixture as another individual object, and soforth. Any type of object that can appear or be present in a particularhome or household can be recognized and labeled by the object detectionmodule 512.

The object detection module 512 provides the identified and recognizedobjects to the room classification module 514. The room classificationmodule 514 can compute or determine a room classification of the roomdepicted in the image depicting the room in the home 501 based on theidentified and recognized objects received from the object detectionmodule 512. In some implementations, the room classification module 514compares the objects received from the object detection module 512 to aplurality of lists of expected objects each associated with a differentroom classification that is stored in data structures 300. For example,the room classification module 514 can compare the objects detected bythe object detection module 512 to a first list of expected objectsassociated with a living room classification. The room classificationmodule 514 can compute a quantity or percentage of the objects that aredetected by the object detection module 512 and that are included in thefirst list. The room classification module 514 can assign a relevancyscore to the first list. The room classification module 514 can thensimilarly compare the objects detected by the object detection module512 to a second list of expected objects associated with another roomclassification (e.g., a kitchen). The room classification module 514 canthen compute a quantity or percentage of the objects that are detectedby the object detection module 512 and that are included in the secondlist and can assign a relevancy score to the second list. The roomclassification module 514 can identify which of the lists that arestored in the data structures 300 is associated with a highest relevancyscore. The room classification module 514 can then determine or computethe room classification of the room depicted in the image depicting theroom in the home 501 based on the room classification associated withthe identified list of expected objects with the highest relevancyscore.

In another implementation, the room classification module 514 canimplement one or more machine learning techniques to classify a room ina home or household. The machine learning techniques can implement aclassifier neural network that is trained to establish a relationshipbetween one or more features of an image of a room in a home with acorresponding room classification.

During training, the machine learning technique of the roomclassification module 514 receives a given training image (e.g., amonocular image or video depicting a real-world room in a home, such asan image of a living room or bedroom) from training image data stored indata structures 300. The room classification module 514 applies one ormore machine learning techniques on the given training image. The roomclassification module 514 extracts one or more features from the giventraining image to estimate a room classification for the room depictedin the image or video. For example, the room classification module 514obtains the given training image depicting a room in a home and extractsfeatures from the image that correspond to the real-world objects thatappear in the room. In some cases, rather than receiving an imagedepicting a room in a home, the room classification module 514 receivesa list or plurality of objects detected by another module or machinelearning technique. The room classification module 514 is trained todetermine a room classification based on the features of the objectsreceived from the other machine learning technique.

The room classification module 514 determines the relative positions ofthe detected real-world objects and/or features of the image depictingthe room in the home. The room classification module 514 then estimatesor computes a room classification based on the relative positions of thedetected real-world objects and/or features of the image depicting theroom in the home. The room classification module 514 obtains a known orpredetermined ground-truth room classification of the room depicted inthe training image from the training data. The room classificationmodule 514 compares the estimated room classification with the groundtruth room classification. Based on a difference threshold of thecomparison, the room classification module 514 updates one or morecoefficients or parameters and obtains one or more additional trainingimages of a room in a home. In some cases, the room classificationmodule 514 is first trained on a set of images associated with one roomclassification and is then trained on another set of images associatedwith another room classification.

After a specified number of epochs or batches of training images havebeen processed and/or when a difference threshold (computed as afunction of a difference between the estimated classification and theground-truth classification) reaches a specified value, the roomclassification module 514 completes training and the parameters andcoefficients of the room classification module 514 are stored as atrained machine learning technique or trained classifier.

In some cases, multiple classifiers are trained in parallel orsequentially on different sets of training data corresponding todifferent room classifications. For example, a first classifier can betrained to classify a living room based on a first set of trainingimages that depict different living room features. A second classifiercan be trained to classify a bedroom based on a second set of trainingimages that depict different bedroom features. The bedroom classifiercan also provide an output that provides an estimated age of the personassociated with the bedroom. In this way, the bedroom classifier canindicate that the bedroom is a master or guest bedroom (if the estimatedage is within a first range), a nursery (if the estimated age is withina second range of ages that are younger than the ages of the firstrange), a toddler room (if the estimated age range is within a thirdrange), or a teenager room (if the estimated age range is within afourth range). In such circumstances, multiple classifiers can operateon a same input image and can generate a room classification with agiven score that indicates how accurate the generated roomclassification is. The room classification module 514 obtains the scoresand classifications from all of the multiple classifiers and thenassigns the room classification to the input image depicting the room ina home 501 based on the room classification with the highest score.

In an example, after training, the room classification module 514receives an input image depicting a room in a home 501 as a single RGBimage from a client device 102 or as a video of multiple images. Theroom classification module 514 applies the trained machine learningtechnique(s) to the received input image to extract one or more featuresand to generate a prediction or estimation or room classification of theimage depicting a room in a home 501.

The room classification module 514 provides the room classification tothe expected object module 516. The expected object module 516 obtains alist or plurality of expected objects stored in the data structures 300that is associated with the room classification. For example, theexpected object module 516 obtains list of expected objects (furnitureitems) including: headboard, footboard and mattress frame, mattress andbox springs, mattress pad, sheets and pillowcases, blankets, quilts,comforter, bedspread, duvet, bedskirt, sleeping pillows, specialtypillows, decorative pillows, pillow covers and shams, throws (blankets),draperies, rods, brackets, valances, window shades, blinds, shutters,nightstands, occasional tables; lamps: floor, table, hanging; wallsconces, alarm clock, radio, plants and plant containers, vases,flowers, candles, candleholders, artwork, posters, prints, photos,frames, photo albums, decorative objects and knick-knacks, dressers andclothing, armoire, closet, TV cabinet, chairs, loveseat, chaise lounge,ottoman, bookshelves, decorative ledges, books, magazines, bookends,trunk, bench, writing desk, vanity table, mirrors, rugs, jewelry boxesand jewelry, storage boxes, baskets, trays, telephone; television, cablebox, satellite box, DVD player and videos, tablets, nightlight.

The expected object module 516 compares the listed items from theretrieved list of expected objects with the objects detected by theobject detection module 512 and/or with the objects identified by themachine learning technique implemented by the room classification module514. The expected object module 516 can determine that a particularexpected object is missing from the list of detected objects. Forexample, the expected object module 516 can determine that the roomclassification is a bedroom and that a bed furniture item is missingfrom the list of objects provided by the object detection module 512 orroom classification module 514. In such cases, the expected objectmodule 516 can select a bed as a furniture item to recommend to a userto purchase for inclusion in the room depicted in the image captured bythe client device 102. Specifically, the expected object module 516provides the identifier of the bed furniture item to the AR itemselection module 519. The AR item selection module 519 can then searchfor and retrieve an AR representation of the bed furniture item from alist of AR representations of beds and can instruct the imagemodification module 518 to incorporate the AR representation of the bedfurniture item into the image captured by the client device 102. In somecases, the AR item selection module 519 provides an indication of whereto place the AR representation relative to one or more other real-worldobjects depicted in the image. After the AR representation isincorporated into the image, the image display module 520 generates fordisplay an image that includes the AR representation of the bedfurniture item together with other real-world objects (real-worldfurniture items) depicted in the image captured by the client device102.

As another example, the expected object module 516 can determine that aparticular expected object is missing from the list of detected objects,such as an appliance is missing from the kitchen that is depicted in theimage. For example, the expected object module 516 can determine thatthe room classification is a kitchen (e.g., in response to detecting asink and stove among objects depicted in a video) and that a mixerkitchen appliance is missing from the list of objects provided by theobject detection module 512 or room classification module 514. In suchcases, the expected object module 516 can select a mixer appliance as anappliance item to recommend to a user to purchase for inclusion in theroom depicted in the image captured by the client device 102.Specifically, the expected object module 516 provides the identifier ofthe mixer appliance to the AR item selection module 519. The AR itemselection module 519 can then search for and retrieve an ARrepresentation of the mixer appliance from a list of AR representationsof mixer appliances and can instruct the image modification module 518to incorporate the AR representation of the mixer appliance into theimage captured by the client device 102. In some cases, the AR itemselection module 519 provides an indication of where to place the ARrepresentation relative to one or more other real-world objects depictedin the image. After the AR representation is incorporated into theimage, the image display module 520 generates for display an image thatincludes the AR representation of the mixer appliance together withother real-world objects (real-world furniture items) depicted in theimage captured by the client device 102. The client device 102 canreceive a user input that selects the AR representation and in responsecompletes a purchase transaction for the mixer appliance associated withthe AR representation.

As another example, the expected object module 516 can determine that aparticular expected object matches a given object from the list ofdetected objects, such as an appliance in the kitchen. For example, theexpected object module 516 can determine that the room classification isa kitchen (e.g., in response to detecting a refrigerator among objectsdepicted in a video) and that the refrigerator appliance is included inthe list of objects provided by the object detection module 512 or roomclassification module 514. In such cases, the expected object module 516can retrieve a recipe associated with the kitchen appliance from thelist. Specifically, the AR item selection module 519 can then search forand retrieve an AR representation of the recipe and can instruct theimage modification module 518 to incorporate the AR representation ofthe recipe into the image captured by the client device 102, such as toplace the recipe on top of or next to the refrigerator depicted in theimage. In some cases, the AR representation of the recipe is providedtogether with the AR representation of the mixer appliance that isincorporated into the image.

In some implementations, the expected object module 516 can identifymultiple objects that are in the expected list of objects for the roomclassification and which are missing from the list of objects providedby the object detection module 512 or room classification module 514. Insuch circumstances, the expected object module 516 can assign a rank orscore to each of the missing objects and can select a set of two orthree expected objects associated with higher scores or ranks than othernon-selected expected objects that are missing. The score can beassigned based on an importance level of the expected object that isstored in association with each expected object in the list stored inthe data structures 300. For example, the expected object module 516 candetermine that the room classification is a living room and that a sofa,coffee table, and rug furniture items are missing from the list ofobjects provided by the object detection module 512 or roomclassification module 514. In such cases, the expected object module 516can determine that the sofa and coffee table furniture items have higherscores than the rug furniture item and can in response select only thesofa and coffee table as furniture items to recommend to a user topurchase for inclusion in the room depicted in the image captured by theclient device 102 and can exclude the rug furniture item. The expectedobject module 516 provides the identifier of the sofa and coffee tablefurniture items to the AR item selection module 519. The AR itemselection module 519 can then search for and retrieve AR representationsof the sofa and coffee table furniture items from a list of ARrepresentations and can instruct the image modification module 518 toincorporate the AR representations of the sofa and coffee tablefurniture items into the image captured by the client device 102. Insome cases, the AR item selection module 519 provides an indication ofwhere to place the AR representations relative to one or more otherreal-world world objects depicted in the image. After being incorporatedinto the image, the image display module 520 generates for display animage that includes the AR representations of the sofa and coffee tablefurniture items together with other real-world objects depicted in theimage captured by the client device 102.

The depth reconstruction module 517 receives depth map data 502 from adepth sensor or depth camera of the client device 102. The depth mapdata 502 is associated with the image or video being processed by theroom classification module 514 and the expected object module 516. Thedepth reconstruction module 517 can generate a three-dimensional (3D)mesh representation or reconstruction of the room depicted in the imagecaptured by the client device 102. The depth reconstruction module 517can provide the 3D mesh representation or reconstruction of the room tothe expected object module 516.

Based on the 3D mesh representation or reconstruction, the expectedobject module 516 can further refine which objects from the expectedobject list to recommend to the user to purchase. For example, theexpected object module 516 can determine that the room classification isa living room and that furniture items including a sofa, coffee table,and rug are missing from the list of objects provided by the objectdetection module 512 or room classification module 514. The expectedobject module 516 can also process the 3D mesh representation of theroom to compute an amount of available physical space remaining in theroom depicted in the image. In response, the expected object module 516can determine that only the coffee table furniture item can physicallyfit within the dimensions of the room and that the room cannotphysically fit all three missing furniture items. Namely, the room canonly fit the coffee table furniture item and not the sofa and the rugfurniture items. In such cases, the expected object module 516 can inresponse select only the coffee table furniture item as objects torecommend to a user to purchase for inclusion in the room depicted inthe image captured by the client device 102. The expected object module516 provides the identifier of the coffee table furniture item to the ARitem selection module 519. The AR item selection module 519 can thensearch for and retrieve an AR representation of the coffee tablefurniture item from a list of AR representations of objects that fitwithin the dimensions of the room and can instruct the imagemodification module 518 to incorporate the AR representation of thecoffee table furniture item into the image captured by the client device102. In some cases, the AR item selection module 519 provides anindication of where to place the AR representation relative to one ormore other real-world world objects (furniture items) depicted in theimage based on the 3D mesh representation or reconstruction. After theAR representation is incorporated into the image, the image displaymodule 520 generates for display an image that includes the ARrepresentation of the coffee table furniture item together with otherreal-world objects depicted in the image captured by the client device102.

As another example, based on the 3D mesh representation orreconstruction, the expected object module 516 can further refine whichobjects from the expected object list to recommend to the user topurchase that fit better within the physical space of the room. Forexample, the expected object module 516 can determine that the roomclassification is a living room and that a sofa is included in the listof objects provided by the object detection module 512 or roomclassification module 514. Namely, the expected object module 516 candetermine that a sofa matches an object in an expected list of objectsfor the living room. The expected object module 516 can also process the3D mesh representation of the room to compute an amount of availablephysical space in the room depicted in the image and how much space thesofa consumes or takes up. In response, the expected object module 516can determine that another object of the same type (e.g., a differentsofa) that has different physical dimensions (larger or smaller than thedetected object) can physically fit better (satisfies one or more fitparameters of the 3D mesh representation or reconstruction) within thedimensions of the room. In such cases, the expected object module 516can in response select an alternate sofa as an object to recommend to auser to purchase for inclusion in the room depicted in the imagecaptured by the client device 102. The expected object module 516provides the identifier of the sofa to the AR item selection module 519.The AR item selection module 519 can then search for and retrieve an ARrepresentation of the sofa from a list of AR representations of objectsthat fit within the dimensions of the room and can instruct the imagemodification module 518 to incorporate the AR representation of the sofainto the image captured by the client device 102. In this case, the ARitem selection module 519 provides an indication to replace an existingreal-world object depicted in the image with the AR representation thathas been selected. This allows the user to see how a different sofawould look in the room if it were purchased to replace the existing sofathat is in the room.

In some implementations, the expected object module 516 can receive anage range of a person associated with the room classification of theroom depicted in the image captured by the client device 102. Theexpected object module 516 can identify a list of objects associatedwith the age range of the person, such as a list of toys correspondingto the age range. The expected object module 516 provides the identifierof the list of objects (e.g., the list of toys) to the AR item selectionmodule 519. The AR item selection module 519 can then search for andretrieve AR representations of the list of toys from a list of ARrepresentations and can instruct the image modification module 518 toincorporate the AR representations of the toys into the image capturedby the client device 102. In some cases, the AR item selection module519 provides an indication of where to place the AR representationsrelative to one or more other real-world world objects depicted in theimage, such as on the floor of the room depicted in the image. After theAR representations are incorporated into the image, the image displaymodule 520 generates for display an image that includes the ARrepresentations of the toys together with other real-world objectsdepicted in the image captured by the client device 102. The clientdevice 102 can receive a user input that selects one or more of the ARrepresentations of the toys. In response, the client device 102completes a purchase transaction for the corresponding toy associatedwith the selected AR representation.

FIGS. 6-9 are diagrammatic representations of outputs of the ARrecommendation system, in accordance with some examples. Specifically,as shown in FIG. 6, the AR recommendation system 224 receives an imageor video 600 that depicts a room in a home. The AR recommendation system224 applies one or more machine learning techniques to detect andrecognize one or more real-world objects that are depicted in the imageor video 600. For example, the AR recommendation system 224 detects andrecognizes a speaker 630 and a television 610 among other objects (e.g.,a sofa, a rug, a flower pot, a coffee table, and so forth).

Based on the detected and recognized objects, the AR recommendationsystem 224 determines that the room depicted in the image or video 600corresponds to a living room classification. In response, the ARrecommendation system 224 obtains a list of expected objects associatedwith the living room classification. The AR recommendation system 224determines that the television 610 matches a given one of the objects inthe list of expected objects. In response, the AR recommendation system224 displays a first indicator (e.g., a blue border) around thereal-world television 610 that is depicted in the image or video 600.The object that is matched is associated with a video item that isavailable for electronic consumption. In such cases, the ARrecommendation system 224 obtains an AR element or representation 620 ofthe video item. The AR recommendation system 224 displays the AR elementor representation 620 of the video item (including an image of the videoitem, a title, and access information) next to or on top of thetelevision 610. The AR recommendation system 224 can receive a userselection of the AR element or representation 620 and in response the ARrecommendation system 224 can complete a purchase transaction or canotherwise access the video item corresponding to the AR element orrepresentation 620. In some cases, the AR recommendation system 224displays the video item corresponding to the AR element orrepresentation 620 on the television 610 in response to receiving theuser selection of the AR element or representation 620.

As another example, the AR recommendation system 224 also determinesthat the speaker 630 matches a given one of the objects in the list ofexpected objects. In response, the AR recommendation system 224 displaysa second indicator (e.g., a red border) around the real-world speaker630 that is depicted in the image or video 600. The object that ismatched is associated with an audio item that is available forelectronic consumption. In such cases, the AR recommendation system 224obtains an AR element or representation 640 of the audio item. The ARrecommendation system 224 displays the AR element or representation 640of the audio item (including an image of the audio item, a title, andaccess information) next to or on top of the speaker 630 (together withthe AR element or representation 620 displayed next to or on top of thetelevision 610). The AR recommendation system 224 can receive a userselection of the AR element or representation 640 and in response the ARrecommendation system 224 can complete a purchase transaction or canotherwise access the audio item corresponding to the AR element orrepresentation 640. In some cases, the AR recommendation system 224instructs the speaker 630 to begin playing back the audio itemcorresponding to the AR element or representation 640 in response toreceiving the user selection of the AR element or representation 640.

As shown in FIG. 7, the AR recommendation system 224 receives an imageor video 700 that depicts a room in a home. The AR recommendation system224 applies one or more machine learning techniques to detect andrecognize one or more real-world objects that are depicted in the imageor video 700. For example, the AR recommendation system 224 detects andrecognizes a desk and a computer monitor 710 among other objects.

Based on the detected and recognized objects, the AR recommendationsystem 224 determines that the room depicted in the image or video 700corresponds to an office room classification. In response, the ARrecommendation system 224 obtains a list of expected objects associatedwith the office room classification. The AR recommendation system 224determines that the computer monitor 710 matches a given one of theobjects in the list of expected objects. In response, the ARrecommendation system 224 displays a first indicator (e.g., a yellowborder) around the real-world computer monitor 710 that is depicted inthe image or video 700. The object that is matched is associated with avideo game item that is available for electronic consumption. In suchcases, the AR recommendation system 224 obtains an AR element orrepresentation 720 of the video game item. The AR recommendation system224 displays the AR element or representation 720 of the video game item(including an image of the video game item, a title, and accessinformation) next to or on top of the computer monitor 710. The ARrecommendation system 224 can receive a user selection of the AR elementor representation 720 and in response the AR recommendation system 224can complete a purchase transaction or can otherwise access the videogame item corresponding to the AR element or representation 720.

In some example, the AR recommendation system 224 determines the list ofexpected items associated with the office room classification is missingone or more of the objects that are detected and recognized in the imageor video 700. For example, the AR recommendation system 224 determinesthat an office chair is in the list of expected items but is missingfrom the list of objects that have been detected and recognized. Inresponse, the AR recommendation system 224 generates a 3D meshrepresentation or reconstruction of the room depicted in the image orvideo 700. The AR recommendation system 224 identifies an office chairthat is available for purchase and that satisfies one or more fitparameters based on the 3D mesh representation or reconstruction of theroom. Namely, the AR recommendation system 224 identifies an officechair that fits within the available physical space and dimensions ofthe real-world office table depicted in the image or video 700. The ARrecommendation system 224 searches for and generates an ARrepresentation 730 corresponding to the identified office chair that isavailable for purchase. The AR recommendation system 224 determines thatthe office chair belongs next to the office table depicted in the imageor video 700. In response, the AR recommendation system 224 displays theAR representation 730 of the office chair next to the real-world tabledepicted in the image or video 700. This allows the user to see how anoffice chair would look in the user's office room before purchasing theoffice chair.

In some cases, the AR recommendation system 224 determines that anoffice chair (a piece of furniture) exists in the image or video 700.The AR recommendation system 224 obtains the 3D mesh representation orreconstruction of the room and determines that the existing office chairfails to satisfy one or more fit parameters for the room. For example,the AR recommendation system 224 determines that the existing officechair is too large or too small for the room. In such cases, the ARrecommendation system 224 identifies an office chair (an alternate ordifferent furniture item) that is available for purchase and thatsatisfies one or more fit parameters based on the 3D mesh representationor reconstruction of the room. Namely, the AR recommendation system 224identifies an office chair furniture item that fits within the availablephysical space and dimensions of the real-world office table depicted inthe image or video 700. The AR recommendation system 224 searches forand generates an AR representation 730 corresponding to the identifiedoffice chair furniture item that is available for purchase. The ARrecommendation system 224 replaces the existing office chair in theimage or video 700 with the AR representation 730 of the office chairfurniture item. In some cases, the AR recommendation system 224positions the AR representation 730 of the office chair in the sameplace as the existing office chair after the real-world office chair isremoved or deleted from the image or video 700. This allows the user tosee how a different office chair (piece of furniture) would look in theuser's office room before purchasing the office chair.

As shown in FIG. 8, the AR recommendation system 224 receives an imageor video 800 that depicts a room in a home. The AR recommendation system224 applies one or more machine learning techniques to detect andrecognize one or more real-world objects that are depicted in the imageor video 800. For example, the AR recommendation system 224 detects andrecognizes a closet 810, among other objects.

Based on the detected and recognized objects, the AR recommendationsystem 224 determines that the room depicted in the image or video 800includes a closet 810. The AR recommendation system 224 identifiesdifferent portions of the closet 810. For example, the AR recommendationsystem 224 determines that a first portion of the closet 810 includesvarious garments of a first type (e.g., pants) and that a second portionof the closet 810 includes various garments of a second type (e.g.,shirts). The AR recommendation system 224 can apply one or more trainedmachine learning techniques to recognize the type of garments that areincluded in the closet 810. For example, in response to detecting thatthe image or video 800 includes a closet object, the AR recommendationsystem 224 can provide the closet 810 to a trained machine learningtechnique to segment out and recognize the garment types that areincluded in the closet 810.

After receiving or determining the garment types included in the closet810, the AR recommendation system 224 selects a first portion 812 of thecloset 810. The AR recommendation system 224 displays a prompt informingthe user that a virtual try-on augmented reality experience is availablefor the selected first portion 812. In an example, the AR recommendationsystem 224 displays an indicator around the first portion 812 tohighlight the first portion 812. In response to receiving a userselection of the indicator, the AR recommendation system 224 activates avirtual try-on experience. In the virtual try-on experience a 3D virtualshopping assistant 820 is displayed by the AR recommendation system 224.Specifically, the AR recommendation system 224 retrieves an ARrepresentation of a 3D virtual shopping assistant 820 and positions the3D virtual shopping assistant 820 next to (adjacent to) the closet 810that is depicted in the image or video 800.

The 3D virtual shopping assistant 820 can search for and select variousgarments of the type corresponding to the garment type of the selectedportion of the closet 810. For example, if the selected first portion812 corresponds to a shirt garment type, the 3D virtual shoppingassistant 820 searches for various shirts that have a fit matching a fitof the user specified in a user profile. The 3D virtual shoppingassistant 820 displays one or more AR elements representing the variousgarments of the garment type (e.g., AR elements representing differentshirts). The 3D virtual shopping assistant 820 can receive a user inputthat selects a given one of the displayed AR elements. In response, the3D virtual shopping assistant 820 activates a front facing camera of theclient device 102. The 3D virtual shopping assistant 820 waits for atorso of the user to be depicted in an image captured by the clientdevice 102 and in response, the 3D virtual shopping assistant 820displays a shirt AR element corresponding to the selected one of thedisplayed AR element. The shirt AR element is displayed on top of thetorso of the user allowing the user to visualize how the real-worldgarment will look on the user, such as in a virtual try-on experience.The user can communicate with the 3D virtual shopping assistant 820(e.g., verbally or by selecting on-screen options) to purchase theselected AR garment or to try on a different garment. In some cases, theuser can select a different portion of the closet 810. In response, thegarment type of the selected portion is determined and used to searchfor and recommend AR garments of the same garment type.

As shown in FIG. 9, the AR recommendation system 224 receives an imageor video 900 that depicts a room in a home. The AR recommendation system224 applies one or more machine learning techniques to detect andrecognize one or more real-world objects that are depicted in the imageor video 900. For example, the AR recommendation system 224 detects andrecognizes a bed and nightstand, among other objects.

Based on the detected and recognized objects, the AR recommendationsystem 224 determines that the room depicted in the image or video 900corresponds to a bedroom room classification. In response, the ARrecommendation system 224 obtains a list of expected objects associatedwith the bedroom room classification. For example, the AR recommendationsystem 224 determines an age range for the bedroom depicted in the imageand can identify a list of toys corresponding to the determined agerange. In such cases, the AR recommendation system 224 obtains an ARelement or representation 910 of the list of toys corresponding to theage range of the bedroom depicted in the image or video 900. The ARrecommendation system 224 displays the AR element or representation 910of the toys on the floor, such as next to another real-world objectdepicted in the image or video 900. The AR recommendation system 224 canreceive a user selection of the AR element or representation 910 and inresponse the AR recommendation system 224 can complete a purchasetransaction to obtain the toy corresponding to the AR element orrepresentation 910.

As another example, the AR recommendation system 224 receives an imageor video that depicts a room in a home. The AR recommendation system 224applies one or more machine learning techniques to detect and recognizeone or more real-world objects that are depicted in the image or video.For example, the AR recommendation system 224 detects and recognizes avanity and shower, among other objects.

Based on the detected and recognized objects, the AR recommendationsystem 224 determines that the room depicted in the image or videocorresponds to a bathroom room classification. In response, the ARrecommendation system 224 obtains a list of expected objects associatedwith the bathroom room classification. For example, the ARrecommendation system 224 identifies products within the bathroom andidentifies a list of other bathroom products (e.g., soaps and perfumes)that may be of interest to the user. In such cases, the ARrecommendation system 224 obtains an AR element or representation of thelist of other bathroom products. The AR recommendation system 224displays the AR element or representation of the other bathroom productson the vanity next to related products, such as next to anotherreal-world object depicted in the image or video. The AR recommendationsystem 224 can receive a user selection of the AR element orrepresentation and in response the AR recommendation system 224 cancomplete a purchase transaction to obtain the product corresponding tothe AR element or representation.

FIG. 10 is a flowchart of a process 1000, in accordance with someexamples. Although the flowchart can describe the operations as asequential process, many of the operations can be performed in parallelor concurrently. In addition, the order of the operations may bere-arranged. A process is terminated when its operations are completed.A process may correspond to a method, a procedure, and the like. Thesteps of methods may be performed in whole or in part, may be performedin conjunction with some or all of the steps in other methods, and maybe performed by any number of different systems or any portion thereof,such as a processor included in any of the systems.

At operation 1001, a client device 102 receives a video that includes adepiction of a one or more objects in a room within a home, as discussedabove.

At operation 1002, the client device 102 determines a roomclassification for the room by processing the one or more objectsdepicted in the video, as discussed above.

At operation 1003, the client device 102 selects one or more augmentedreality items available for purchase based on the room classificationand the one or more objects depicted in the video, as discussed above.

At operation 1004, the client device 102 generates, for display withinthe video, the one or more augmented reality items that have beenselected at a display position within the video corresponding to the oneor more objects depicted in the video, as discussed above.

Machine Architecture

FIG. 11 is a diagrammatic representation of the machine 1100 withinwhich instructions 1108 (e.g., software, a program, an application, anapplet, an app, or other executable code) for causing the machine 1100to perform any one or more of the methodologies discussed herein may beexecuted. For example, the instructions 1108 may cause the machine 1100to execute any one or more of the methods described herein. Theinstructions 108 transform the general, non-programmed machine 1100 intoa particular machine 1100 programmed to carry out the described andillustrated functions in the manner described. The machine 1100 mayoperate as a standalone device or may be coupled (e.g., networked) toother machines. In a networked deployment, the machine 1100 may operatein the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 1100 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a personal digital assistant (PDA), anentertainment media system, a cellular telephone, a smartphone, a mobiledevice, a wearable device (e.g., a smartwatch), a smart home device(e.g., a smart appliance), other smart devices, a web appliance, anetwork router, a network switch, a network bridge, or any machinecapable of executing the instructions 1108, sequentially or otherwise,that specify actions to be taken by the machine 1100. Further, whileonly a single machine 1100 is illustrated, the term “machine” shall alsobe taken to include a collection of machines that individually orjointly execute the instructions 1108 to perform any one or more of themethodologies discussed herein. The machine 1100, for example, maycomprise the client device 102 or any one of a number of server devicesforming part of the messaging server system 108. In some examples, themachine 1100 may also comprise both client and server systems, withcertain operations of a particular method or algorithm being performedon the server-side and with certain operations of the particular methodor algorithm being performed on the client-side.

The machine 1100 may include processors 1102, memory 1104, andinput/output (I/O) components 1138, which may be configured tocommunicate with each other via a bus 1140. In an example, theprocessors 1102 (e.g., a Central Processing Unit (CPU), a ReducedInstruction Set Computing (RISC) Processor, a Complex Instruction SetComputing (CISC) Processor, a Graphics Processing Unit (GPU), a DigitalSignal Processor (DSP), an Application Specific Integrated Circuit(ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor,or any suitable combination thereof) may include, for example, aprocessor 1106 and a processor 1110 that execute the instructions 1108.The term “processor” is intended to include multi-core processors thatmay comprise two or more independent processors (sometimes referred toas “cores”) that may execute instructions contemporaneously. AlthoughFIG. 11 shows multiple processors 1102, the machine 1100 may include asingle processor with a single-core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiples cores, or any combinationthereof.

The memory 1104 includes a main memory 1112, a static memory 1114, and astorage unit 1116, all accessible to the processors 1102 via the bus1140. The main memory 1104, the static memory 1114, and the storage unit1116 store the instructions 1108 embodying any one or more of themethodologies or functions described herein. The instructions 1108 mayalso reside, completely or partially, within the main memory 1112,within the static memory 1114, within a machine-readable medium withinthe storage unit 1116, within at least one of the processors 1102 (e.g.,within the processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 1100.

The I/O components 1138 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1138 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1138 mayinclude many other components that are not shown in FIG. 11. In variousexamples, the I/O components 1138 may include user output components1124 and user input components 1126. The user output components 1124 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light-emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The userinput components 1126 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and force of touches or touch gestures, or other tactile inputcomponents), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 1138 may include biometriccomponents 1128, motion components 1130, environmental components 1132,or position components 1134, among a wide array of other components. Forexample, the biometric components 1128 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye-tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 1130 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope).

The environmental components 1132 include, for example, one or cameras(with still image/photograph and video capabilities), illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment.

With respect to cameras, the client device 102 may have a camera systemcomprising, for example, front cameras on a front surface of the clientdevice 102 and rear cameras on a rear surface of the client device 102.The front cameras may, for example, be used to capture still images andvideo of a user of the client device 102 (e.g., “selfies”), which maythen be augmented with augmentation data (e.g., filters) describedabove. The rear cameras may, for example, be used to capture stillimages and videos in a more traditional camera mode, with these imagessimilarly being augmented with augmentation data. In addition to frontand rear cameras, the client device 102 may also include a 3600 camerafor capturing 360° photographs and videos.

Further, the camera system of a client device 102 may include dual rearcameras (e.g., a primary camera as well as a depth-sensing camera), oreven triple, quad or penta rear camera configurations on the front andrear sides of the client device 102. These multiple cameras systems mayinclude a wide camera, an ultra-wide camera, a telephoto camera, a macrocamera, and a depth sensor, for example.

The position components 1134 include location sensor components (e.g., aGPS receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1138 further include communication components 1136operable to couple the machine 1100 to a network 1120 or devices 1122via respective coupling or connections. For example, the communicationcomponents 1136 may include a network interface component or anothersuitable device to interface with the network 1120. In further examples,the communication components 1136 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1122 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 1136 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1136 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1136, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., main memory 1112, static memory 1114, andmemory of the processors 1102) and storage unit 1116 may store one ormore sets of instructions and data structures (e.g., software) embodyingor used by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 1108), when executedby processors 1102, cause various operations to implement the disclosedexamples.

The instructions 1108 may be transmitted or received over the network1120, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components1136) and using any one of several well-known transfer protocols (e.g.,hypertext transfer protocol (HTTP)). Similarly, the instructions 108 maybe transmitted or received using a transmission medium via a coupling(e.g., a peer-to-peer coupling) to the devices 1122.

Software Architecture

FIG. 12 is a block diagram 1200 illustrating a software architecture1204, which can be installed on any one or more of the devices describedherein. The software architecture 1204 is supported by hardware such asa machine 1202 that includes processors 1220, memory 1226, and I/Ocomponents 1238. In this example, the software architecture 1204 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 1204 includes layerssuch as an operating system 1212, libraries 1210, frameworks 1208, andapplications 1206. Operationally, the applications 1206 invoke API calls1250 through the software stack and receive messages 1252 in response tothe API calls 1250.

The operating system 1212 manages hardware resources and provides commonservices. The operating system 1212 includes, for example, a kernel1214, services 1216, and drivers 1222. The kernel 1214 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 1214 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionality. The services 1216 canprovide other common services for the other software layers. The drivers1222 are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1222 can include display drivers,camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flashmemory drivers, serial communication drivers (e.g., USB drivers), WI-FI®drivers, audio drivers, power management drivers, and so forth.

The libraries 1210 provide a common low-level infrastructure used by theapplications 1206. The libraries 1210 can include system libraries 1218(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 1210 can include APIlibraries 1224 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries (e.g., SQLite to provide various relational databasefunctions), web libraries (e.g., WebKit to provide web browsingfunctionality), and the like. The libraries 1210 can also include a widevariety of other libraries 1228 to provide many other APIs to theapplications 1206.

The frameworks 1208 provide a common high-level infrastructure that isused by the applications 1206. For example, the frameworks 1208 providevarious graphical user interface (GUI) functions, high-level resourcemanagement, and high-level location services. The frameworks 1208 canprovide a broad spectrum of other APIs that can be used by theapplications 1206, some of which may be specific to a particularoperating system or platform.

In an example, the applications 1206 may include a home application1236, a contacts application 1230, a browser application 1232, a bookreader application 1234, a location application 1242, a mediaapplication 1244, a messaging application 1246, a game application 1248,and a broad assortment of other applications such as an externalapplication 1240. The applications 1206 are programs that executefunctions defined in the programs. Various programming languages can beemployed to create one or more of the applications 1206, structured in avariety of manners, such as object-oriented programming languages (e.g.,Objective-C, Java, or C++) or procedural programming languages (e.g., Cor assembly language). In a specific example, the external application1240 (e.g., an application developed using the ANDROID™ or IOS™ softwaredevelopment kit (SDK) by an entity other than the vendor of theparticular platform) may be mobile software running on a mobileoperating system such as IOS™, ANDROID™, WINDOWS® Phone, or anothermobile operating system. In this example, the external application 1240can invoke the API calls 1250 provided by the operating system 1212 tofacilitate functionality described herein.

Glossary

“Carrier signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Client device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops,multi-processor systems, microprocessor-based or programmable consumerelectronics, game consoles, set-top boxes, or any other communicationdevice that a user may use to access a network.

“Communication network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions.

Components may constitute either software components (e.g., codeembodied on a machine-readable medium) or hardware components. A“hardware component” is a tangible unit capable of performing certainoperations and may be configured or arranged in a certain physicalmanner. In various examples, one or more computer systems (e.g., astandalone computer system, a client computer system, or a servercomputer system) or one or more hardware components of a computer system(e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwarecomponent that operates to perform certain operations as describedherein.

A hardware component may also be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware component may include dedicated circuitry or logic that ispermanently configured to perform certain operations. A hardwarecomponent may be a special-purpose processor, such as afield-programmable gate array (FPGA) or an application specificintegrated circuit (ASIC). A hardware component may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software), may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein.

Considering examples in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instance of time and to constitutea different hardware component at a different instance of time.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inexamples in which multiple hardware components are configured orinstantiated at different times, communications between such hardwarecomponents may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors 1102 orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API). The performance ofcertain of the operations may be distributed among the processors, notonly residing within a single machine, but deployed across a number ofmachines. In some examples, the processors or processor-implementedcomponents may be located in a single geographic location (e.g., withina home environment, an office environment, or a server farm). In otherexamples, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“Computer-readable storage medium” refers to both machine-storage mediaand transmission media. Thus, the terms include both storagedevices/media and carrier waves/modulated data signals. The terms“machine-readable medium,” “computer-readable medium” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure.

“Ephemeral message” refers to a message that is accessible for atime-limited duration. An ephemeral message may be a text, an image, avideo and the like. The access time for the ephemeral message may be setby the message sender. Alternatively, the access time may be a defaultsetting or a setting specified by the recipient. Regardless of thesetting technique, the message is transitory.

“Machine storage medium” refers to a single or multiple storage devicesand media (e.g., a centralized or distributed database, and associatedcaches and servers) that store executable instructions, routines anddata. The term shall accordingly be taken to include, but not be limitedto, solid-state memories, and optical and magnetic media, includingmemory internal or external to processors. Specific examples ofmachine-storage media, computer-storage media and device-storage mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), FPGA, andflash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks Theterms “machine-storage medium,” “device-storage medium,”“computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangiblemedium that is capable of storing, encoding, or carrying theinstructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

Changes and modifications may be made to the disclosed examples withoutdeparting from the scope of the present disclosure. These and otherchanges or modifications are intended to be included within the scope ofthe present disclosure, as expressed in the following claims.

What is claimed is:
 1. A method comprising: receiving, by one or moreprocessors, a video that includes a depiction of one or more objects ina room within a home; determining a room classification for the room byprocessing the one or more objects depicted in the video; selecting oneor more augmented reality items available for purchase based on the roomclassification and the one or more objects depicted in the video; andgenerating, for display within the video, the one or more augmentedreality items that have been selected at a display position within thevideo corresponding to the one or more objects depicted in the video. 2.The method of claim 1, further comprising: obtaining a plurality ofexpected objects associated with the room classification; detecting theone or more objects depicted in the video using an object recognitionprocess; and comparing the detected one or more objects depicted in thevideo to the plurality of expected objects.
 3. The method of claim 2,further comprising: based on the comparing, identifying a given expectedobject from the plurality of expected objects that is excluded from thedetected one or more objects; and searching for an augmented realityitem corresponding to the given expected object to be selected as theone or more augmented reality items available for purchase.
 4. Themethod of claim 2, further comprising: identifying an expected objectfrom the plurality of expected objects that matches a given one of thedetected one or more objects; and searching for an augmented realityitem corresponding to the expected object to be selected as the one ormore augmented reality items available for purchase.
 5. The method ofclaim 1, wherein the room classification corresponds to a living room,further comprising: detecting a television as a first of the one or moreobjects depicted in the video; searching for a video item available forconsumption on the television; and causing a first augmented realityrepresentation of the video item to be displayed next to or on top ofthe television in the video.
 6. The method of claim 5, furthercomprising: detecting a speaker as a second of the one or more objectsdepicted in the video; searching for a music item available forconsumption through the speaker; and causing a second augmented realityrepresentation of the music item to be displayed next to or on top ofthe speaker in the video together with the first augmented reality item.7. The method of claim 5, further comprising: generating athree-dimensional (3D) mesh representation of the living room; obtaininga plurality of furniture items corresponding to a living roomclassification; detecting that a given one of the plurality of furnitureitems is missing from the detected one or more objects depicted in thevideo; determining, based on the 3D mesh representation of the livingroom, that space is available for the given one of the plurality offurniture items; and causing a second augmented reality representationof the given one of the plurality of furniture items to be displayed ata position corresponding to the space that is available together withthe first augmented reality item.
 8. The method of claim 7, furthercomprising: receiving input that selects the second augmented realityrepresentation; and performing a purchase transaction to complete apurchase for the given one of the plurality of furniture items.
 9. Themethod of claim 1, wherein the room classification corresponds to abedroom or an office, further comprising: generating a three-dimensional(3D) mesh representation of the bedroom or the office; obtaining aplurality of furniture items corresponding to a bedroom or officeclassification; detecting that a given one of the plurality of furnitureitems is missing from the detected one or more objects depicted in thevideo; determining, based on the 3D mesh representation of the bedroomor the office, that space is available for the given one of theplurality of furniture items; and causing a first augmented realityrepresentation of the given one of the plurality of furniture items tobe displayed at a position corresponding to the space that is available.10. The method of claim 1, wherein the room classification correspondsto a bedroom, further comprising: detecting that a closet is includedamong the one or more objects depicted in the video; and causing avisual indicator to be presented over the closet depicted in the videoin response to detecting that the closet is included among the one ormore objects depicted in the video.
 11. The method of claim 10, furthercomprising: causing an option to access a virtual clothing try-onaugmented reality experience in response to detecting that the closet isincluded among the one or more objects depicted in the video.
 12. Themethod of claim 10, further comprising: generating, for display withinthe video, a three-dimensional (3D) virtual shopping assistant adjacentto the closet that is detected in the video; detecting that a portion ofthe closet includes a particular garment type; causing the portion to behighlighted by the visual indicator; and causing the 3D virtual shoppingassistant to recommend for purchase clothing corresponding to theparticular garment type.
 13. The method of claim 1, wherein the roomclassification corresponds to an office, further comprising: detecting acomputer monitor as a first of the one or more objects depicted in thevideo; searching for a video game item available for consumption on thecomputer monitor; and causing a first augmented reality representationof the video game item to be displayed next to or on top of the computermonitor in the video.
 14. The method of claim 1, wherein the roomclassification corresponds to a bedroom or an office, furthercomprising: generating a three-dimensional (3D) mesh representation ofthe bedroom or the office; detecting that a given one of the detectedone or more objects depicted in the video includes a given furnitureitem that fails to satisfy one or more fit parameters of the 3D meshrepresentation; identifying, based on the 3D mesh representation of thebedroom or the office, a recommended furniture item that satisfies theone or more fit parameters of the 3D mesh representation and is of asame type as the given furniture item included in the video; and causinga first augmented reality representation of the recommended furnitureitem to be displayed at a position corresponding to space that isavailable.
 15. The method of claim 1, wherein the room classificationcorresponds to a kitchen, further comprising: detecting a sink and stoveas first and second of the one or more objects depicted in the video;searching for a kitchen appliance that is excluded from the one or moreobjects depicted in the video; and causing a first augmented realityrepresentation of the kitchen appliance to be displayed within thevideo, the first augmented reality representation enabling purchase ofthe kitchen appliance.
 16. The method of claim 15, further comprising:detecting a refrigerator as a third of the one or more objects depictedin the video; searching for a recipe; and causing a second augmentedreality representation of the recipe to be displayed within the videonext to or on top of the refrigerator.
 17. The method of claim 1,wherein the room classification corresponds to a bedroom, furthercomprising: determining an age range associated with the bedroom basedon the one or more objects depicted in the video; searching for one ormore toys associated with the age range; and causing a first augmentedreality representation of a given one of the one or more toys to bedisplayed within the video, the first augmented reality representationenabling purchase of the given one of the one or more toys.
 18. Themethod of claim 1, further comprising training a neural networkclassifier to determine the room classification by: receiving trainingdata comprising a plurality of training images and ground truth roomclassifications for each of the plurality of training images, each ofthe plurality of training monocular images depicting a different room ina home; applying the neural network classifier to a first training imageof the plurality of training images to estimate a room classification ofthe room in the home depicted in the first training image; computing adeviation between the estimated room classification and the ground truthroom classification associated with the first training image; updatingparameters of the neural network classifier based on the computeddeviation; and repeating the applying, computing and updating steps fora set of the plurality of training images.
 19. A system comprising: aprocessor configured to perform operations comprising: receiving a videothat includes a depiction of one or more objects in a room within ahome; determining a room classification for the room by processing theone or more objects depicted in the video; selecting one or moreaugmented reality items available for purchase based on the roomclassification and the one or more objects depicted in the video; andgenerating, for display within the video, the one or more augmentedreality items that have been selected at a display position within thevideo corresponding to the one or more objects depicted in the video.20. A non-transitory machine-readable storage medium that includesinstructions that, when executed by one or more processors of a machine,cause the machine to perform operations comprising: receiving a videothat includes a depiction of one or more objects in a room within ahome; determining a room classification for the room by processing theone or more objects depicted in the video; selecting one or moreaugmented reality items available for purchase based on the roomclassification and the one or more objects depicted in the video; andgenerating, for display within the video, the one or more augmentedreality items that have been selected at a display position within thevideo corresponding to the one or more objects depicted in the video.